In this paper we evaluate the distributional impact of carbon pricing in Ireland via a number of different measures, Excise Duties, Carbon Taxes and the EU Emissions Trading Scheme, utilising information contained in the OECD Effective Carbon Rate (ECR) database together with the PRICES model. Essential household energy consumption constitutes a significant portion of spending, particularly for lower-income households, indicating regressive expenditure patterns across income brackets. The immediate impact of carbon pricing on household budgets varies based on their reliance on various fuels for heating and transportation (direct impact), as well as the emissions associated with other goods and services (indirect impact). Carbon footprints vary widely among households, with higher-income ones generally emitting less than lower-income ones as a percentage of their income. Although carbon footprints primarily dictate the burdens of carbon pricing, other factors such as the uneven application of carbon pricing policies and disparities in emissions between industries and fuel types also influence the equation. Despite the necessity for substantial carbon price hikes to meet climate targets, the effects on household budgets during the 2012-2021 period were relatively modest. Carbon pricing reforms typically exhibited regressive trends, disproportionately affecting lower-income households relative to their earnings. We modelled also a number of different reforms utilising the revenue generated by the additional carbon revenues. The net impact in terms of winners and losers depended very significantly upon the both the nature of the expenditure and upon the share of revenue used.

Carbon taxes can be regressive for multiple reasons. Differences in what households consume, the carbon-intensive of what they consume, the technology they use, and how much of their income they spend all contribute towards the regressivity of a carbon tax. This paper quantifies the relative importance of these factors for carbon tax regressivity in 6 EU countries. The comparison across countries highlights that differences in what households consume are important, but not always the most important factor in driving carbon tax regressivity. Differences in how much of their income households spend are always important and often the most important factor. Differences in the carbon intensity of consumption are more relevant in Eastern European countries and the importance of heating and transportation technology depends on the country. This paper concludes with implications for designing effective mitigation strategies and cross-country policy transfer, highlighting that the source of carbon tax regressivity and effective strategies differs across countries.

This paper decomposes and compares the distributional impact of uniform national carbon taxes across six EU countries. We quantify the contribution of the key determinants of the carbon tax burden to its impact on inequality and regressivity indicators. We identify large cross-country differences in carbon tax burdens, their composition, and the drivers of the within-country distributional impact. A carbon tax is regressive in all countries, but carbon tax burdens and their impact on income inequality are larger in poorer countries of our sample. Cross-country differences in the primary driver of carbon tax regressivity suggest that the most effective policy lever to mitigate carbon tax regressivity differs across countries. Differences in the composition of the consumption basket play an important role in most countries, but not all. Differences in savings rates play the most important role in the wealthier countries of our sample. The carbon intensity of consumption plays a larger role in the poorer countries of our sample. Overall, this article suggests that differences in the structure of carbon tax incidence and the drivers of its distributional impact across countries pose a challenge to cross-country policy learning, and highlights the need for in-depth country-level and comparative analysis.

This paper lays out an approach, and a research agenda, for assessing the impact of carbon pricing on household budgets, and of possible compensatory government transfers that can be financed through carbon-tax revenues. It relies on a rich set of available data and policy models and combines them in a way that is informative for mapping the gains and losses at the household level in the short term as countries transition to a low-carbon economy. A particular focus is on linking information on carbon emissions and consumption patterns (which is needed for quantifying carbon-tax burdens), with income data and tax-transfer policy models (needed for assessing government policies that aim to cushion or offset carbon-tax burdens). The approach is illustrated for a carbon-tax scenario based on a recent proposal in Lithuania. Results confirm that direct burdens from higher fuel prices fall disproportionately on lower-income households. But indirect effects, from higher prices of goods other than fuel, are sizeable and broadly "flat" across the income distribution, which dampens regressivity. Low-income households are also found to respond more strongly to rising prices, reducing their burdens and, hence, regressivity. The total effect is only mildly regressive. Recycling carbon-tax revenues back to households allows considerable scope for avoiding or cushioning losses for large parts of the population, and existing policy models can be used to design compensation measures that facilitate majority support for carbon tax packages.

This paper develops a microsimulation model to simulate the distributional impact of price changes using Household Budget Survey data, income survey data and an Input Output Model. The primary purpose is to describe the model components. The secondary purpose is to demonstrate one component of the model by assessing the distributional and welfare impact of recent price changes in Pakistan. Over the period of November 2020 to November 2022, headline inflation 41.5 percent, with food and transportation prices increasing most. The analysis shows that despite large increases in energy prices, the importance of energy prices for the welfare losses due to inflation is limited because energy budget shares are small and inflation is relatively low. The overall distributional impact of recent price changes is mildly progressive, but household welfare is impacted significantly irrespective of households position along the income distribution. The biggest driver of welfare losses at the bottom of the income distribution was food price inflation, while inflation in other goods and services was the biggest driver at the top. To compensate households for increased living costs, transfers would need to be on average 40 percent of pre-inflation expenditure, assuming constant incomes. Behavioural responses to price changes have a negligible impact on the overall welfare cost to households.

This paper disentangles the distributional and welfare impact of price changes since the start of the cost of living crisis for a subset of European countries with different welfare regimes and price changes. It decomposes the impact of inflation and measures welfare changes using the compensating variation and equivalent incomes in a cross-national comparative perspective. The impact of inflation depends on good-specific price increases and budget shares. Budget shares for necessities (e.g. food, domestic fuel, electricity) are higher in poorer countries and for poorer people. Higher price growth in these necessities has resulted in higher inflation in poorer countries. Counter to the media narrative, the distributional impact is less substantial than expected. A significant cross-country variability exists, however, in inflation levels, composition and relative rates across the distribution. Similar levels of inflation regressivity result from different interplays between the level and disproportionality of inflation along the income distribution. We quantify the compensating variation of inflation with a relatively small behavioural component due to the preponderance of necessities among the goods with high price changes. An important factor concerning the potential impact on households is the savings rate. Households with already low savings are disproportionally feeling the impact on their expenditure.

During recent crisis, wage subsidies played a major role in sheltering firms and households from economic shocks. During COVID-19, most workers were affected and many liberal welfare states introduced new temporary wage subsidies to protected workers' earnings and employment (OECD, 2021). New wage subsidies marked a departure from the structure of traditional income support payments and required reform. This paper uses simulated datasets to assess the structure and incentives of the Irish COVID-19 wage subsidy scheme (CWS) under five designs. We use a nowcasting approach to update 2017 microdata, producing a near real time picture of the labour market at the peak of the crisis. Using microsimulation modelling, we assess the impact of different designs on income replacement, work incentives and income inequality. Our findings suggest that pro rata designs support middle earners more and flat rate designs support low earners more. We find evidence for strong work disincentives under all designs, though flat rate designs perform better. Disincentives are primarily driven by generous unemployment payments and work related costs. The impact of design on income inequality depends on the generosity of payments. Earnings related pro rata designs were associated to higher market earnings inequality. The difference in inequality levels falls once benefits, taxes and work related costs are considered. In our discussion, we turn to transaction costs, the rationale for reform and reintegration of CWS. We find some support for the claim that design changes were motivated by political considerations. We suggest that establishing permanent wage subsidies based on sectorial turnover rules could offer enhanced protection to middle-and high-earners and reduce uncertainty, the need for reform, and the risk of politically motivated designs.

We evaluate the COVID-19 resilience of a Continental welfare regime by nowcasting the implications of the shock and its associated policy responses on the distribution of household incomes over the whole of 2020. Our approach relies on a dynamic microsimulation modelling that combines a household income generation model estimated on the latest EU-SILC wave with novel nowcasting techniques to calibrate the simulations using external macro controls which reflect the macroeconomic climate during the crisis. We focus on Luxembourg, a country that introduced minor tweaks to the existing tax-benefit system, which has a strong social insurance focus that gave certainty during the crisis. We find the system was well-equipped ahead of the crisis to cushion household incomes against job losses. The income-support policy changes were effective in cushioning household incomes and mitigating an increase in income inequality, allowing average household disposable income and inequality levels to bounce back to pre-crisis levels in the last quarter of 2020. The share of labour incomes dropped, but was compensated by an increase in benefits, reflecting the cushioning effect of the transfer system. Overall market incomes dropped and became more unequal. Their disequalizing evolution was matched by an increase in redistribution, driven by an increase in the generosity of benefits and larger access to benefits. The nowcasting model is a “near” real-time analysis and decision support tool to monitor the recovery, scalable to other countries with high applicability for policymakers.